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Found 12 papers, 8 papers with code
Speech Wikimedia: A 77 Language Multilingual Speech Dataset
The Speech Wikimedia Dataset is a publicly available compilation of audio with transcriptions extracted from Wikimedia Commons.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.
LSH methods for data deduplication in a Wikipedia artificial dataset
This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset.
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
MLPerf Tiny Benchmark
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications.
Data Engineering for Everyone
This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations.
MLPerf Mobile Inference Benchmark
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks.
Benchmarking TinyML Systems: Challenges and Direction
In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.
MLPerf Training Benchmark
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
